AIM: The aim of this study was to develop a novel long non-coding RNA (lncRNA) expression signature to accurately predict early recurrence for patients with hepatocellular carcinoma (HCC) after curative resection. METHODS: Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple lncRNAs with differential expression between early recurrence (ER) and non-early recurrence (non-ER) groups of patients with HCC. Least absolute shrinkage and selection operator for logistic regression models were used to develop an lncRNA-based classifier for predicting ER in the training set. An independent test set was used to validate the predictive value of this classifier. Furthermore, a co-expression network based on these lncRNAs and its highly related genes was constructed and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of genes in the network were carried out. RESULTS: We identified 10 differentially expressed lncRNAs, including three that were upregulated and seven that were downregulated in the ER group. The lncRNA-based classifier was constructed based on seven lncRNAs (AL035661.1, PART1, AC011632.1, AC109588.1, AL365361.1, LINC00861, and LINC02084), and its accuracy was 0.83 in the training set, 0.87 in the test set, and 0.84 in the total set. Receiver operating characteristic curve analysis showed the area under the curve was 0.741 in the training set, 0.824 in the test set, and 0.765 in the total set. A functional enrichment analysis suggested that the genes highly related to four lncRNAs are involved in the immune system. CONCLUSION: The expression profile of seven lncRNAs can effectively predict ER after surgical resection for HCC.
AIM: The aim of this study was to develop a novel long non-coding RNA (lncRNA) expression signature to accurately predict early recurrence for patients with hepatocellular carcinoma (HCC) after curative resection. METHODS: Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple lncRNAs with differential expression between early recurrence (ER) and non-early recurrence (non-ER) groups of patients with HCC. Least absolute shrinkage and selection operator for logistic regression models were used to develop an lncRNA-based classifier for predicting ER in the training set. An independent test set was used to validate the predictive value of this classifier. Furthermore, a co-expression network based on these lncRNAs and its highly related genes was constructed and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of genes in the network were carried out. RESULTS: We identified 10 differentially expressed lncRNAs, including three that were upregulated and seven that were downregulated in the ER group. The lncRNA-based classifier was constructed based on seven lncRNAs (AL035661.1, PART1, AC011632.1, AC109588.1, AL365361.1, LINC00861, and LINC02084), and its accuracy was 0.83 in the training set, 0.87 in the test set, and 0.84 in the total set. Receiver operating characteristic curve analysis showed the area under the curve was 0.741 in the training set, 0.824 in the test set, and 0.765 in the total set. A functional enrichment analysis suggested that the genes highly related to four lncRNAs are involved in the immune system. CONCLUSION: The expression profile of seven lncRNAs can effectively predict ER after surgical resection for HCC.